Black Friday Analysis

1 Introduction

The dataset here is a sample of the transactions made in a retail store. The store wants to know better the customer purchase behaviour against different products. Specifically, here the problem is a regression problem where we are trying to predict the dependent variable (the amount of purchase) with the help of the information contained in the other variables.

Classification problem can also be settled in this dataset since several variables are categorical, and some other approaches could be “Predicting the age of the consumer” or even “Predict the category of goods bought”. This dataset is also particularly convenient for clustering and maybe find different clusters of consumers within it.

2 Required R - Packages

There are several R packages that useful for analyzing this dataset.

• dplyr, tool frome processing dataset.
• ggplot2, creating graphics.
• plotly, to help make pie chart.
• purrr.

3 Data

This dataset has 12 variables

3.1 Dataset

##   User_ID Product_ID Gender   Age Occupation City_Category
## 1 1000001  P00069042      F  0-17         10             A
## 2 1000001  P00248942      F  0-17         10             A
## 3 1000001  P00087842      F  0-17         10             A
## 4 1000001  P00085442      F  0-17         10             A
## 5 1000002  P00285442      M   55+         16             C
## 6 1000003  P00193542      M 26-35         15             A
##   Stay_In_Current_City_Years Marital_Status Product_Category_1
## 1                          2              0                  3
## 2                          2              0                  1
## 3                          2              0                 12
## 4                          2              0                 12
## 5                         4+              0                  8
## 6                          3              0                  1
##   Product_Category_2 Product_Category_3 Purchase
## 1                 NA                 NA     8370
## 2                  6                 14    15200
## 3                 NA                 NA     1422
## 4                 14                 NA     1057
## 5                 NA                 NA     7969
## 6                  2                 NA    15227

3.2 Data Structure

## Observations: 537,577
## Variables: 12
## $ User_ID                    <int> 1000001, 1000001, 1000001, 1000001, 1…
## $ Product_ID                 <fct> P00069042, P00248942, P00087842, P000…
## $ Gender                     <fct> F, F, F, F, M, M, M, M, M, M, M, M, M…
## $ Age                        <fct> 0-17, 0-17, 0-17, 0-17, 55+, 26-35, 4…
## $ Occupation                 <int> 10, 10, 10, 10, 16, 15, 7, 7, 7, 20, …
## $ City_Category              <fct> A, A, A, A, C, A, B, B, B, A, A, A, A…
## $ Stay_In_Current_City_Years <fct> 2, 2, 2, 2, 4+, 3, 2, 2, 2, 1, 1, 1, …
## $ Marital_Status             <int> 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1…
## $ Product_Category_1         <int> 3, 1, 12, 12, 8, 1, 1, 1, 1, 8, 5, 8,…
## $ Product_Category_2         <int> NA, 6, NA, 14, NA, 2, 8, 15, 16, NA, …
## $ Product_Category_3         <int> NA, 14, NA, NA, NA, NA, 17, NA, NA, N…
## $ Purchase                   <int> 8370, 15200, 1422, 1057, 7969, 15227,…

4 Exploratory Data Analysis

## [1] "5891 buyers registered at Black Friday"

4.1 Total Purchase Distribution

4.2 Total Purchase by City

4.3 Total Purchase by City and Gender

4.4 Total Purchase by Gender

4.5 Total Purchase by Age

5 Data Clustering

5.1 Determine The Number of Cluster

5.2 Cluster Model